code: def logit_tmle_grad(x, y, a, h0, h1):
      p = expit(a + x[0] * h0 + x[1] * h1)
      return np.array([-np.mean((y - p) * h0), -np.mean((y - p) * h1)])
  
file_path: causalml/causalml/inference/meta/tmle.py
imports: import logging
  import numpy
  import pandas
  from scipy.optimize import minimize
  from scipy.special import expit, logit
  from scipy.stats import norm
  from sklearn.preprocessing import MinMaxScaler
  from causalml.inference.meta.utils import check_treatment_vector, check_p_conditions, convert_pd_to_np
  from causalml.propensity import calibrate
label_desc: Use image node_id 2 for calling a global function with example usage: logit_tmle_grad(x, y, a, h0, h1) and returns: np